Group recommender systems: A multi-agent solution

VILLAVICENCIO, CHRISTIAN; MONTESERIN, ARIEL; JORGE ANDRES DIAZ PACE; SCHIAFFINO, SILVIA; VILLAVICENCIO, CHRISTIAN; MONTESERIN, ARIEL; JORGE ANDRES DIAZ PACE; SCHIAFFINO, SILVIA

KNOWLEDGE-BASED SYSTEMS

ELSEVIER SCIENCE BV

Año: 2019 vol. 164 p. 436 - 458

0950-7051

Providing recommendations to groups of users has become a promising research area, since many items tend to be consumed by groups of people. Various techniques have been developed aiming at making recommendations to a group as a whole. Most works use aggregation techniques to combine preferences, recommendations or profiles. However, satisfying all group members in an even way still remains as a challenge. To deal with this problem, we propose an extension of a multi-agent approach based on negotiation techniques for group recommendation. In the approach, we use the multilateral Monotonic Concession Protocol (MCP) to combine individual recommendations into a group recommendation. In this work, we extend the MCP protocol to allow users to personalize the behavior of the agents. This extension was evaluated in two different domains (movies and points of interest) with satisfactory results. We compared our approach against different baselines, namely: a preference aggregation algorithm, a recommendation aggregation algorithm, and a simple one-step negotiation. The results show evidence that, when using our negotiation approach, users in the groups are more uniformly satisfied than with traditional aggregation approaches.